Try Snowflake : DEA-C02 valid & accurate questions and answers

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:

1. You're designing a data pipeline in Snowflake that utilizes an external function to perform sentiment analysis on customer reviews using a third-party NLP service. This service charges per request. You need to minimize costs while ensuring timely processing of the reviews.
Which of the following strategies would be most effective in optimizing the cost and performance of your external function?

A) Bypass the external function completely and rely solely on Snowflake's built-in NLP functions for sentiment analysis.
B) Pre-process the customer reviews in Snowflake to filter out irrelevant reviews (e.g., very short reviews or reviews with stop words) before sending them to the external function.
C) Implement rate limiting and error handling in the external service (e.g., AWS Lambda or Azure Function) to gracefully handle API usage limits and prevent excessive charges due to errors.
D) Implement a caching mechanism (e.g., using a Snowflake table or an external cache) to store the sentiment analysis results for frequently occurring reviews or similar text patterns, avoiding redundant API calls.
E) Set 'MAX BATCH_ROWS' to a very high value (e.g., 10000) to maximize the number of rows processed per API call, even if it increases latency for individual reviews.

2. Snowpark DataFrame 'employee_df' contains employee data, including 'employee_id', 'department', and 'salary'. You need to calculate the average salary for each department and also retrieve all the employee details along with the department average salary.
Which of the following approaches is the MOST efficient way to achieve this?

A) Use the 'window' function with 'avg' to compute the average salary per department and include it as a new column in the original DataFrame.
B) Create a separate DataFrame with average salaries per department, then join it back to the original DataFrame.
C) Use 'groupBV to get a dataframe containing average salary by department and then use a Python UDF to iterate through the 'employee_df and add the value to each row
D) Use a correlated subquery within the SELECT statement to calculate the average salary for each department for each employee.
E) Create a temporary table with average salaries per department, then join it back to the original DataFrame.

3. You have a data pipeline that aggregates web server logs hourly. The pipeline loads data into a Snowflake table 'WEB LOGS' which is partitioned by 'event_time'. You notice that queries against this table are slow, especially those that filter on specific time ranges. Analyze the following Snowflake table definition and query pattern and select the options to diagnose and fix the performance issue: Table Definition:

A) Increase the warehouse size to improve query performance.
B) Add a search optimization strategy to the table on the 'event_time' column.
C) Create a materialized view that pre-aggregates the 'status_code' by hour to speed up the aggregation query.
D) Change the table to use clustering on 'event_time' instead of partitioning to improve query performance for range filters.
E) The table is already partitioned by 'event_time' , so there is no need for further optimization.

4. You are loading JSON data into a Snowflake table with a 'VARIANT' column. The JSON data contains nested arrays with varying depths. You need to extract specific values from the nested arrays and load them into separate columns in your Snowflake table. Which approach would provide the BEST performance and flexibility?

A) Use Snowpipe with auto-ingest, loading directly into the table with the 'VARIANT column. Define data quality checks with pre-load data transformation.
B) Load the entire JSON into a 'VARIANT column and then use SQL with nested 'FLATTEN' functions to extract the desired values during query time.
C) Create a view with nested 'FLATTEN' functions to extract the values from the 'VARIANT column. The view serves as the source for further transformations.
D) Use a stored procedure to parse the JSON data and insert values into the table row by row.
E) Use a 'COPY' command with a 'TRANSFORM' clause that uses JavaScript UDFs to parse the JSON and extract the values during the load process. Load the extracted values directly into the target columns.

5. A data engineering team uses Snowflake to analyze website clickstream data stored in AWS S3. The data is partitioned by year and month in the S3 bucket. They need to query the data frequently for reporting purposes but don't want to ingest the entire dataset into Snowflake due to storage costs and infrequent full dataset analysis. Which approach is the MOST efficient and cost-effective way to enable querying of this data in Snowflake?

A) Use Snowflake's COPY INTO command to ingest data directly from S3 into a Snowflake table on a scheduled basis.
B) Create a Snowflake internal stage, copy the necessary files into the stage, and then load the data into a Snowflake table.
C) Create a Snowflake external stage pointing to the S3 bucket, define an external table on the stage, and use partitioning metadata to optimize queries.
D) Create a Snowpipe pointing to the S3 bucket and ingest the data continuously into a Snowflake table.
E) Load all the data into a Snowflake table and create a materialized view on top of the table to pre-aggregate the data for reporting.

Solutions: